Find the Balance Between MPP Databases and Spark for Analytical Processing

Presented by

Paige Roberts, Open Source Relations Manager, Vertica, and David Menninger, SVP & Research Director, Ventana Research

About this talk

Both Apache Spark and massively parallel processing (MPP) databases are designed for the demands of analytical workloads. Each has strengths related to the full data science workflow, from consolidating data from many siloes, to deploying and managing machine learning models. Understanding the power of each technology, and the cost and performance trade-offs between them can help you optimize your analytics architecture to get the best of both. Learn when using Spark accelerates data processing, and when it spreads far beyond what you want to maintain. Learn when an MPP database can provide blazing fast analytics, and when it can fail to meet your needs. Most of all, learn how these two powerful technologies can combine to create a perfect balance of power, cost, and performance.

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The Vertica Analytics Platform is built to handle the most demanding analytic use cases and is trusted by thousands of leading data-driven enterprises around the world, including Etsy, Bank of America, Intuit, Uber and more. Vertica delivers speed, scale and reliability on mission-critical analytics at a lower total cost of ownership than legacy systems. All based on the same powerful, unified architecture, the Vertica Analytics Platform provides you with the broadest range of deployment models, so that you have complete choice as your analytical needs evolve. Deploy Vertica on-premise, in the clouds (AWS, Azure and GCP), on Apache Hadoop, or as a hybrid model. Find more information on Vertica at